512 lines
55 KiB
HTML
512 lines
55 KiB
HTML
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<h1><a href="notebooks_v1.html">Notebooks API</a> . <a href="notebooks_v1.projects.html">projects</a> . <a href="notebooks_v1.projects.locations.html">locations</a> . <a href="notebooks_v1.projects.locations.schedules.html">schedules</a></h1>
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<h2>Instance Methods</h2>
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<p class="toc_element">
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<code><a href="#close">close()</a></code></p>
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<p class="firstline">Close httplib2 connections.</p>
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<p class="toc_element">
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<code><a href="#create">create(parent, body=None, scheduleId=None, x__xgafv=None)</a></code></p>
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<p class="firstline">Creates a new Scheduled Notebook in a given project and location.</p>
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<p class="toc_element">
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<code><a href="#delete">delete(name, x__xgafv=None)</a></code></p>
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<p class="firstline">Deletes schedule and all underlying jobs</p>
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<p class="toc_element">
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<code><a href="#get">get(name, x__xgafv=None)</a></code></p>
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<p class="firstline">Gets details of schedule</p>
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<p class="toc_element">
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<code><a href="#list">list(parent, filter=None, orderBy=None, pageSize=None, pageToken=None, x__xgafv=None)</a></code></p>
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<p class="firstline">Lists schedules in a given project and location.</p>
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<p class="toc_element">
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<code><a href="#list_next">list_next(previous_request, previous_response)</a></code></p>
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<p class="firstline">Retrieves the next page of results.</p>
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<p class="toc_element">
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<code><a href="#trigger">trigger(name, body=None, x__xgafv=None)</a></code></p>
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<p class="firstline">Triggers execution of an existing schedule.</p>
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<h3>Method Details</h3>
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<div class="method">
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<code class="details" id="close">close()</code>
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<pre>Close httplib2 connections.</pre>
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</div>
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<div class="method">
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<code class="details" id="create">create(parent, body=None, scheduleId=None, x__xgafv=None)</code>
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<pre>Creates a new Scheduled Notebook in a given project and location.
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Args:
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parent: string, Required. Format: `parent=projects/{project_id}/locations/{location}` (required)
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body: object, The request body.
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The object takes the form of:
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{ # The definition of a schedule.
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"createTime": "A String", # Output only. Time the schedule was created.
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"cronSchedule": "A String", # Cron-tab formatted schedule by which the job will execute. Format: minute, hour, day of month, month, day of week, e.g. 0 0 * * WED = every Wednesday More examples: https://crontab.guru/examples.html
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"description": "A String", # A brief description of this environment.
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"displayName": "A String", # Output only. Display name used for UI purposes. Name can only contain alphanumeric characters, hyphens '-', and underscores '_'.
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"executionTemplate": { # The description a notebook execution workload. # Notebook Execution Template corresponding to this schedule.
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"acceleratorConfig": { # Definition of a hardware accelerator. Note that not all combinations of `type` and `core_count` are valid. Check [GPUs on Compute Engine](https://cloud.google.com/compute/docs/gpus) to find a valid combination. TPUs are not supported. # Configuration (count and accelerator type) for hardware running notebook execution.
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"coreCount": "A String", # Count of cores of this accelerator.
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"type": "A String", # Type of this accelerator.
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},
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"containerImageUri": "A String", # Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
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"dataprocParameters": { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
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"cluster": "A String", # URI for cluster used to run Dataproc execution. Format: `projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}`
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},
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"inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: `gs://{bucket_name}/{folder}/{notebook_file_name}` Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb`
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"jobType": "A String", # The type of Job to be used on this execution.
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"kernelSpec": "A String", # Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
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"labels": { # Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
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"a_key": "A String",
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},
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"masterType": "A String", # Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. The following types are supported: - `n1-standard-4` - `n1-standard-8` - `n1-standard-16` - `n1-standard-32` - `n1-standard-64` - `n1-standard-96` - `n1-highmem-2` - `n1-highmem-4` - `n1-highmem-8` - `n1-highmem-16` - `n1-highmem-32` - `n1-highmem-64` - `n1-highmem-96` - `n1-highcpu-16` - `n1-highcpu-32` - `n1-highcpu-64` - `n1-highcpu-96` Alternatively, you can use the following legacy machine types: - `standard` - `large_model` - `complex_model_s` - `complex_model_m` - `complex_model_l` - `standard_gpu` - `complex_model_m_gpu` - `complex_model_l_gpu` - `standard_p100` - `complex_model_m_p100` - `standard_v100` - `large_model_v100` - `complex_model_m_v100` - `complex_model_l_v100` Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPU](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
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"outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: `gs://{bucket_name}/{folder}` Ex: `gs://notebook_user/scheduled_notebooks`
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"parameters": "A String", # Parameters used within the 'input_notebook_file' notebook.
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"paramsYamlFile": "A String", # Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml`
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"scaleTier": "A String", # Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
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"serviceAccount": "A String", # The email address of a service account to use when running the execution. You must have the `iam.serviceAccounts.actAs` permission for the specified service account.
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"vertexAiParameters": { # Parameters used in Vertex AI JobType executions. # Parameters used in Vertex AI JobType executions.
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"env": { # Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
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"a_key": "A String",
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},
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"network": "A String", # The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
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},
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},
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"name": "A String", # Output only. The name of this schedule. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}`
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"recentExecutions": [ # Output only. The most recent execution names triggered from this schedule and their corresponding states.
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{ # The definition of a single executed notebook.
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"createTime": "A String", # Output only. Time the Execution was instantiated.
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"description": "A String", # A brief description of this execution.
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"displayName": "A String", # Output only. Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.
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"executionTemplate": { # The description a notebook execution workload. # execute metadata including name, hardware spec, region, labels, etc.
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"acceleratorConfig": { # Definition of a hardware accelerator. Note that not all combinations of `type` and `core_count` are valid. Check [GPUs on Compute Engine](https://cloud.google.com/compute/docs/gpus) to find a valid combination. TPUs are not supported. # Configuration (count and accelerator type) for hardware running notebook execution.
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"coreCount": "A String", # Count of cores of this accelerator.
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"type": "A String", # Type of this accelerator.
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},
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"containerImageUri": "A String", # Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
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"dataprocParameters": { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
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"cluster": "A String", # URI for cluster used to run Dataproc execution. Format: `projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}`
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},
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"inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: `gs://{bucket_name}/{folder}/{notebook_file_name}` Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb`
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"jobType": "A String", # The type of Job to be used on this execution.
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"kernelSpec": "A String", # Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
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"labels": { # Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
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"a_key": "A String",
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},
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"masterType": "A String", # Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. The following types are supported: - `n1-standard-4` - `n1-standard-8` - `n1-standard-16` - `n1-standard-32` - `n1-standard-64` - `n1-standard-96` - `n1-highmem-2` - `n1-highmem-4` - `n1-highmem-8` - `n1-highmem-16` - `n1-highmem-32` - `n1-highmem-64` - `n1-highmem-96` - `n1-highcpu-16` - `n1-highcpu-32` - `n1-highcpu-64` - `n1-highcpu-96` Alternatively, you can use the following legacy machine types: - `standard` - `large_model` - `complex_model_s` - `complex_model_m` - `complex_model_l` - `standard_gpu` - `complex_model_m_gpu` - `complex_model_l_gpu` - `standard_p100` - `complex_model_m_p100` - `standard_v100` - `large_model_v100` - `complex_model_m_v100` - `complex_model_l_v100` Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPU](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
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"outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: `gs://{bucket_name}/{folder}` Ex: `gs://notebook_user/scheduled_notebooks`
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"parameters": "A String", # Parameters used within the 'input_notebook_file' notebook.
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"paramsYamlFile": "A String", # Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml`
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"scaleTier": "A String", # Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
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"serviceAccount": "A String", # The email address of a service account to use when running the execution. You must have the `iam.serviceAccounts.actAs` permission for the specified service account.
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"vertexAiParameters": { # Parameters used in Vertex AI JobType executions. # Parameters used in Vertex AI JobType executions.
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"env": { # Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
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"a_key": "A String",
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},
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"network": "A String", # The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
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},
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},
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"jobUri": "A String", # Output only. The URI of the external job used to execute the notebook.
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"name": "A String", # Output only. The resource name of the execute. Format: `projects/{project_id}/locations/{location}/executions/{execution_id}`
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"outputNotebookFile": "A String", # Output notebook file generated by this execution
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"state": "A String", # Output only. State of the underlying AI Platform job.
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"updateTime": "A String", # Output only. Time the Execution was last updated.
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},
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],
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"state": "A String",
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"timeZone": "A String", # Timezone on which the cron_schedule. The value of this field must be a time zone name from the tz database. TZ Database: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones Note that some time zones include a provision for daylight savings time. The rules for daylight saving time are determined by the chosen tz. For UTC use the string "utc". If a time zone is not specified, the default will be in UTC (also known as GMT).
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"updateTime": "A String", # Output only. Time the schedule was last updated.
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}
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scheduleId: string, Required. User-defined unique ID of this schedule.
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x__xgafv: string, V1 error format.
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Allowed values
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1 - v1 error format
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2 - v2 error format
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Returns:
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An object of the form:
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{ # This resource represents a long-running operation that is the result of a network API call.
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"done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
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"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
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"code": 42, # The status code, which should be an enum value of google.rpc.Code.
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"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
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{
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"a_key": "", # Properties of the object. Contains field @type with type URL.
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},
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],
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"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
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},
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"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
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"a_key": "", # Properties of the object. Contains field @type with type URL.
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},
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"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
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"response": { # The normal response of the operation in case of success. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
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"a_key": "", # Properties of the object. Contains field @type with type URL.
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},
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}</pre>
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</div>
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<div class="method">
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<code class="details" id="delete">delete(name, x__xgafv=None)</code>
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<pre>Deletes schedule and all underlying jobs
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Args:
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name: string, Required. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}` (required)
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x__xgafv: string, V1 error format.
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Allowed values
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1 - v1 error format
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2 - v2 error format
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Returns:
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An object of the form:
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{ # This resource represents a long-running operation that is the result of a network API call.
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"done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
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"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
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"code": 42, # The status code, which should be an enum value of google.rpc.Code.
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"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
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{
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"a_key": "", # Properties of the object. Contains field @type with type URL.
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},
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],
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"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
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},
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"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
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"a_key": "", # Properties of the object. Contains field @type with type URL.
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},
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"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
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"response": { # The normal response of the operation in case of success. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
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"a_key": "", # Properties of the object. Contains field @type with type URL.
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},
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}</pre>
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</div>
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<div class="method">
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<code class="details" id="get">get(name, x__xgafv=None)</code>
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<pre>Gets details of schedule
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Args:
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name: string, Required. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}` (required)
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x__xgafv: string, V1 error format.
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Allowed values
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1 - v1 error format
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2 - v2 error format
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Returns:
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An object of the form:
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{ # The definition of a schedule.
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"createTime": "A String", # Output only. Time the schedule was created.
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"cronSchedule": "A String", # Cron-tab formatted schedule by which the job will execute. Format: minute, hour, day of month, month, day of week, e.g. 0 0 * * WED = every Wednesday More examples: https://crontab.guru/examples.html
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"description": "A String", # A brief description of this environment.
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"displayName": "A String", # Output only. Display name used for UI purposes. Name can only contain alphanumeric characters, hyphens '-', and underscores '_'.
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"executionTemplate": { # The description a notebook execution workload. # Notebook Execution Template corresponding to this schedule.
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"acceleratorConfig": { # Definition of a hardware accelerator. Note that not all combinations of `type` and `core_count` are valid. Check [GPUs on Compute Engine](https://cloud.google.com/compute/docs/gpus) to find a valid combination. TPUs are not supported. # Configuration (count and accelerator type) for hardware running notebook execution.
|
|
"coreCount": "A String", # Count of cores of this accelerator.
|
|
"type": "A String", # Type of this accelerator.
|
|
},
|
|
"containerImageUri": "A String", # Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
|
|
"dataprocParameters": { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
|
|
"cluster": "A String", # URI for cluster used to run Dataproc execution. Format: `projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}`
|
|
},
|
|
"inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: `gs://{bucket_name}/{folder}/{notebook_file_name}` Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb`
|
|
"jobType": "A String", # The type of Job to be used on this execution.
|
|
"kernelSpec": "A String", # Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
|
|
"labels": { # Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
|
|
"a_key": "A String",
|
|
},
|
|
"masterType": "A String", # Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. The following types are supported: - `n1-standard-4` - `n1-standard-8` - `n1-standard-16` - `n1-standard-32` - `n1-standard-64` - `n1-standard-96` - `n1-highmem-2` - `n1-highmem-4` - `n1-highmem-8` - `n1-highmem-16` - `n1-highmem-32` - `n1-highmem-64` - `n1-highmem-96` - `n1-highcpu-16` - `n1-highcpu-32` - `n1-highcpu-64` - `n1-highcpu-96` Alternatively, you can use the following legacy machine types: - `standard` - `large_model` - `complex_model_s` - `complex_model_m` - `complex_model_l` - `standard_gpu` - `complex_model_m_gpu` - `complex_model_l_gpu` - `standard_p100` - `complex_model_m_p100` - `standard_v100` - `large_model_v100` - `complex_model_m_v100` - `complex_model_l_v100` Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPU](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
|
|
"outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: `gs://{bucket_name}/{folder}` Ex: `gs://notebook_user/scheduled_notebooks`
|
|
"parameters": "A String", # Parameters used within the 'input_notebook_file' notebook.
|
|
"paramsYamlFile": "A String", # Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml`
|
|
"scaleTier": "A String", # Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
|
|
"serviceAccount": "A String", # The email address of a service account to use when running the execution. You must have the `iam.serviceAccounts.actAs` permission for the specified service account.
|
|
"vertexAiParameters": { # Parameters used in Vertex AI JobType executions. # Parameters used in Vertex AI JobType executions.
|
|
"env": { # Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
|
|
"a_key": "A String",
|
|
},
|
|
"network": "A String", # The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
|
|
},
|
|
},
|
|
"name": "A String", # Output only. The name of this schedule. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}`
|
|
"recentExecutions": [ # Output only. The most recent execution names triggered from this schedule and their corresponding states.
|
|
{ # The definition of a single executed notebook.
|
|
"createTime": "A String", # Output only. Time the Execution was instantiated.
|
|
"description": "A String", # A brief description of this execution.
|
|
"displayName": "A String", # Output only. Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.
|
|
"executionTemplate": { # The description a notebook execution workload. # execute metadata including name, hardware spec, region, labels, etc.
|
|
"acceleratorConfig": { # Definition of a hardware accelerator. Note that not all combinations of `type` and `core_count` are valid. Check [GPUs on Compute Engine](https://cloud.google.com/compute/docs/gpus) to find a valid combination. TPUs are not supported. # Configuration (count and accelerator type) for hardware running notebook execution.
|
|
"coreCount": "A String", # Count of cores of this accelerator.
|
|
"type": "A String", # Type of this accelerator.
|
|
},
|
|
"containerImageUri": "A String", # Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
|
|
"dataprocParameters": { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
|
|
"cluster": "A String", # URI for cluster used to run Dataproc execution. Format: `projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}`
|
|
},
|
|
"inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: `gs://{bucket_name}/{folder}/{notebook_file_name}` Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb`
|
|
"jobType": "A String", # The type of Job to be used on this execution.
|
|
"kernelSpec": "A String", # Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
|
|
"labels": { # Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
|
|
"a_key": "A String",
|
|
},
|
|
"masterType": "A String", # Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. The following types are supported: - `n1-standard-4` - `n1-standard-8` - `n1-standard-16` - `n1-standard-32` - `n1-standard-64` - `n1-standard-96` - `n1-highmem-2` - `n1-highmem-4` - `n1-highmem-8` - `n1-highmem-16` - `n1-highmem-32` - `n1-highmem-64` - `n1-highmem-96` - `n1-highcpu-16` - `n1-highcpu-32` - `n1-highcpu-64` - `n1-highcpu-96` Alternatively, you can use the following legacy machine types: - `standard` - `large_model` - `complex_model_s` - `complex_model_m` - `complex_model_l` - `standard_gpu` - `complex_model_m_gpu` - `complex_model_l_gpu` - `standard_p100` - `complex_model_m_p100` - `standard_v100` - `large_model_v100` - `complex_model_m_v100` - `complex_model_l_v100` Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPU](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
|
|
"outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: `gs://{bucket_name}/{folder}` Ex: `gs://notebook_user/scheduled_notebooks`
|
|
"parameters": "A String", # Parameters used within the 'input_notebook_file' notebook.
|
|
"paramsYamlFile": "A String", # Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml`
|
|
"scaleTier": "A String", # Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
|
|
"serviceAccount": "A String", # The email address of a service account to use when running the execution. You must have the `iam.serviceAccounts.actAs` permission for the specified service account.
|
|
"vertexAiParameters": { # Parameters used in Vertex AI JobType executions. # Parameters used in Vertex AI JobType executions.
|
|
"env": { # Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
|
|
"a_key": "A String",
|
|
},
|
|
"network": "A String", # The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
|
|
},
|
|
},
|
|
"jobUri": "A String", # Output only. The URI of the external job used to execute the notebook.
|
|
"name": "A String", # Output only. The resource name of the execute. Format: `projects/{project_id}/locations/{location}/executions/{execution_id}`
|
|
"outputNotebookFile": "A String", # Output notebook file generated by this execution
|
|
"state": "A String", # Output only. State of the underlying AI Platform job.
|
|
"updateTime": "A String", # Output only. Time the Execution was last updated.
|
|
},
|
|
],
|
|
"state": "A String",
|
|
"timeZone": "A String", # Timezone on which the cron_schedule. The value of this field must be a time zone name from the tz database. TZ Database: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones Note that some time zones include a provision for daylight savings time. The rules for daylight saving time are determined by the chosen tz. For UTC use the string "utc". If a time zone is not specified, the default will be in UTC (also known as GMT).
|
|
"updateTime": "A String", # Output only. Time the schedule was last updated.
|
|
}</pre>
|
|
</div>
|
|
|
|
<div class="method">
|
|
<code class="details" id="list">list(parent, filter=None, orderBy=None, pageSize=None, pageToken=None, x__xgafv=None)</code>
|
|
<pre>Lists schedules in a given project and location.
|
|
|
|
Args:
|
|
parent: string, Required. Format: `parent=projects/{project_id}/locations/{location}` (required)
|
|
filter: string, Filter applied to resulting schedules.
|
|
orderBy: string, Field to order results by.
|
|
pageSize: integer, Maximum return size of the list call.
|
|
pageToken: string, A previous returned page token that can be used to continue listing from the last result.
|
|
x__xgafv: string, V1 error format.
|
|
Allowed values
|
|
1 - v1 error format
|
|
2 - v2 error format
|
|
|
|
Returns:
|
|
An object of the form:
|
|
|
|
{ # Response for listing scheduled notebook job.
|
|
"nextPageToken": "A String", # Page token that can be used to continue listing from the last result in the next list call.
|
|
"schedules": [ # A list of returned instances.
|
|
{ # The definition of a schedule.
|
|
"createTime": "A String", # Output only. Time the schedule was created.
|
|
"cronSchedule": "A String", # Cron-tab formatted schedule by which the job will execute. Format: minute, hour, day of month, month, day of week, e.g. 0 0 * * WED = every Wednesday More examples: https://crontab.guru/examples.html
|
|
"description": "A String", # A brief description of this environment.
|
|
"displayName": "A String", # Output only. Display name used for UI purposes. Name can only contain alphanumeric characters, hyphens '-', and underscores '_'.
|
|
"executionTemplate": { # The description a notebook execution workload. # Notebook Execution Template corresponding to this schedule.
|
|
"acceleratorConfig": { # Definition of a hardware accelerator. Note that not all combinations of `type` and `core_count` are valid. Check [GPUs on Compute Engine](https://cloud.google.com/compute/docs/gpus) to find a valid combination. TPUs are not supported. # Configuration (count and accelerator type) for hardware running notebook execution.
|
|
"coreCount": "A String", # Count of cores of this accelerator.
|
|
"type": "A String", # Type of this accelerator.
|
|
},
|
|
"containerImageUri": "A String", # Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
|
|
"dataprocParameters": { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
|
|
"cluster": "A String", # URI for cluster used to run Dataproc execution. Format: `projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}`
|
|
},
|
|
"inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: `gs://{bucket_name}/{folder}/{notebook_file_name}` Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb`
|
|
"jobType": "A String", # The type of Job to be used on this execution.
|
|
"kernelSpec": "A String", # Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
|
|
"labels": { # Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
|
|
"a_key": "A String",
|
|
},
|
|
"masterType": "A String", # Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. The following types are supported: - `n1-standard-4` - `n1-standard-8` - `n1-standard-16` - `n1-standard-32` - `n1-standard-64` - `n1-standard-96` - `n1-highmem-2` - `n1-highmem-4` - `n1-highmem-8` - `n1-highmem-16` - `n1-highmem-32` - `n1-highmem-64` - `n1-highmem-96` - `n1-highcpu-16` - `n1-highcpu-32` - `n1-highcpu-64` - `n1-highcpu-96` Alternatively, you can use the following legacy machine types: - `standard` - `large_model` - `complex_model_s` - `complex_model_m` - `complex_model_l` - `standard_gpu` - `complex_model_m_gpu` - `complex_model_l_gpu` - `standard_p100` - `complex_model_m_p100` - `standard_v100` - `large_model_v100` - `complex_model_m_v100` - `complex_model_l_v100` Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPU](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
|
|
"outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: `gs://{bucket_name}/{folder}` Ex: `gs://notebook_user/scheduled_notebooks`
|
|
"parameters": "A String", # Parameters used within the 'input_notebook_file' notebook.
|
|
"paramsYamlFile": "A String", # Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml`
|
|
"scaleTier": "A String", # Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
|
|
"serviceAccount": "A String", # The email address of a service account to use when running the execution. You must have the `iam.serviceAccounts.actAs` permission for the specified service account.
|
|
"vertexAiParameters": { # Parameters used in Vertex AI JobType executions. # Parameters used in Vertex AI JobType executions.
|
|
"env": { # Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
|
|
"a_key": "A String",
|
|
},
|
|
"network": "A String", # The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
|
|
},
|
|
},
|
|
"name": "A String", # Output only. The name of this schedule. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}`
|
|
"recentExecutions": [ # Output only. The most recent execution names triggered from this schedule and their corresponding states.
|
|
{ # The definition of a single executed notebook.
|
|
"createTime": "A String", # Output only. Time the Execution was instantiated.
|
|
"description": "A String", # A brief description of this execution.
|
|
"displayName": "A String", # Output only. Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.
|
|
"executionTemplate": { # The description a notebook execution workload. # execute metadata including name, hardware spec, region, labels, etc.
|
|
"acceleratorConfig": { # Definition of a hardware accelerator. Note that not all combinations of `type` and `core_count` are valid. Check [GPUs on Compute Engine](https://cloud.google.com/compute/docs/gpus) to find a valid combination. TPUs are not supported. # Configuration (count and accelerator type) for hardware running notebook execution.
|
|
"coreCount": "A String", # Count of cores of this accelerator.
|
|
"type": "A String", # Type of this accelerator.
|
|
},
|
|
"containerImageUri": "A String", # Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
|
|
"dataprocParameters": { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
|
|
"cluster": "A String", # URI for cluster used to run Dataproc execution. Format: `projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}`
|
|
},
|
|
"inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: `gs://{bucket_name}/{folder}/{notebook_file_name}` Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb`
|
|
"jobType": "A String", # The type of Job to be used on this execution.
|
|
"kernelSpec": "A String", # Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
|
|
"labels": { # Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
|
|
"a_key": "A String",
|
|
},
|
|
"masterType": "A String", # Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. The following types are supported: - `n1-standard-4` - `n1-standard-8` - `n1-standard-16` - `n1-standard-32` - `n1-standard-64` - `n1-standard-96` - `n1-highmem-2` - `n1-highmem-4` - `n1-highmem-8` - `n1-highmem-16` - `n1-highmem-32` - `n1-highmem-64` - `n1-highmem-96` - `n1-highcpu-16` - `n1-highcpu-32` - `n1-highcpu-64` - `n1-highcpu-96` Alternatively, you can use the following legacy machine types: - `standard` - `large_model` - `complex_model_s` - `complex_model_m` - `complex_model_l` - `standard_gpu` - `complex_model_m_gpu` - `complex_model_l_gpu` - `standard_p100` - `complex_model_m_p100` - `standard_v100` - `large_model_v100` - `complex_model_m_v100` - `complex_model_l_v100` Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPU](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
|
|
"outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: `gs://{bucket_name}/{folder}` Ex: `gs://notebook_user/scheduled_notebooks`
|
|
"parameters": "A String", # Parameters used within the 'input_notebook_file' notebook.
|
|
"paramsYamlFile": "A String", # Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml`
|
|
"scaleTier": "A String", # Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
|
|
"serviceAccount": "A String", # The email address of a service account to use when running the execution. You must have the `iam.serviceAccounts.actAs` permission for the specified service account.
|
|
"vertexAiParameters": { # Parameters used in Vertex AI JobType executions. # Parameters used in Vertex AI JobType executions.
|
|
"env": { # Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
|
|
"a_key": "A String",
|
|
},
|
|
"network": "A String", # The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
|
|
},
|
|
},
|
|
"jobUri": "A String", # Output only. The URI of the external job used to execute the notebook.
|
|
"name": "A String", # Output only. The resource name of the execute. Format: `projects/{project_id}/locations/{location}/executions/{execution_id}`
|
|
"outputNotebookFile": "A String", # Output notebook file generated by this execution
|
|
"state": "A String", # Output only. State of the underlying AI Platform job.
|
|
"updateTime": "A String", # Output only. Time the Execution was last updated.
|
|
},
|
|
],
|
|
"state": "A String",
|
|
"timeZone": "A String", # Timezone on which the cron_schedule. The value of this field must be a time zone name from the tz database. TZ Database: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones Note that some time zones include a provision for daylight savings time. The rules for daylight saving time are determined by the chosen tz. For UTC use the string "utc". If a time zone is not specified, the default will be in UTC (also known as GMT).
|
|
"updateTime": "A String", # Output only. Time the schedule was last updated.
|
|
},
|
|
],
|
|
"unreachable": [ # Schedules that could not be reached. For example: ['projects/{project_id}/location/{location}/schedules/monthly_digest', 'projects/{project_id}/location/{location}/schedules/weekly_sentiment']
|
|
"A String",
|
|
],
|
|
}</pre>
|
|
</div>
|
|
|
|
<div class="method">
|
|
<code class="details" id="list_next">list_next(previous_request, previous_response)</code>
|
|
<pre>Retrieves the next page of results.
|
|
|
|
Args:
|
|
previous_request: The request for the previous page. (required)
|
|
previous_response: The response from the request for the previous page. (required)
|
|
|
|
Returns:
|
|
A request object that you can call 'execute()' on to request the next
|
|
page. Returns None if there are no more items in the collection.
|
|
</pre>
|
|
</div>
|
|
|
|
<div class="method">
|
|
<code class="details" id="trigger">trigger(name, body=None, x__xgafv=None)</code>
|
|
<pre>Triggers execution of an existing schedule.
|
|
|
|
Args:
|
|
name: string, Required. Format: `parent=projects/{project_id}/locations/{location}/schedules/{schedule_id}` (required)
|
|
body: object, The request body.
|
|
The object takes the form of:
|
|
|
|
{ # Request for created scheduled notebooks
|
|
}
|
|
|
|
x__xgafv: string, V1 error format.
|
|
Allowed values
|
|
1 - v1 error format
|
|
2 - v2 error format
|
|
|
|
Returns:
|
|
An object of the form:
|
|
|
|
{ # This resource represents a long-running operation that is the result of a network API call.
|
|
"done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
|
|
"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
|
|
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
|
|
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
|
|
{
|
|
"a_key": "", # Properties of the object. Contains field @type with type URL.
|
|
},
|
|
],
|
|
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
|
|
},
|
|
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
|
|
"a_key": "", # Properties of the object. Contains field @type with type URL.
|
|
},
|
|
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
|
|
"response": { # The normal response of the operation in case of success. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
|
|
"a_key": "", # Properties of the object. Contains field @type with type URL.
|
|
},
|
|
}</pre>
|
|
</div>
|
|
|
|
</body></html> |